2,077+ academic papers on AI security, attacks, and defenses
Showing 301–320 of 342 papers
Clear filtersChung-ju Huang, Huiqiang Zhao, Yuanpeng He +5 more
The increasing reliance on cloud-hosted Large Language Models (LLMs) exposes sensitive client data, such as prompts and responses, to potential...
Quanjun Zhang, Chengyu Gao, Yu Han +4 more
The rapid advancement of Large Language Models (LLMs) has led to the emergence of intelligent agents capable of autonomously interacting with...
David Condrey
The proliferation of AI-generated text has intensified the need for reliable authorship verification, yet current output-based methods are...
Zhengqing Yuan, Kaiwen Shi, Zheyuan Zhang +3 more
Scientific research relies on accurate citation for attribution and integrity, yet large language models (LLMs) introduce a new risk: fabricated...
Elzo Brito dos Santos Filho
Autonomous agents based on Large Language Models (LLMs) have evolved from reactive assistants to systems capable of planning, executing actions via...
Kennedy Edemacu, Mohammad Mahdi Shokri
Retrieval-augmented generation (RAG) has emerged as a powerful paradigm for enhancing multimodal large language models by grounding their responses...
Kunpeng Zhang, Dongwei Xiao, Daoyuan Wu +5 more
Deep learning (DL) libraries are widely used in critical applications, where even subtle silent bugs can lead to serious consequences. While existing...
Yuan Liang, Ruobin Zhong, Haoming Xu +46 more
Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic...
Xun Huang, Simeng Qin, Xiaoshuang Jia +6 more
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are...
Jiazheng Quan, Xiaodong Li, Bin Wang +5 more
Large language models (LLMs) have demonstrated strong capabilities in code generation, yet they remain prone to producing security vulnerabilities....
Tian Zhang, Yiwei Xu, Juan Wang +8 more
Large language model (LLM) agents increasingly rely on external tools and retrieval systems to autonomously complete complex tasks. However, this...
Marcus Graves
We introduce Reverse CAPTCHA, an evaluation framework that tests whether large language models follow invisible Unicode-encoded instructions embedded...
Zhonghao Zhan, Krinos Li, Yefan Zhang +1 more
Edge deployment of LLM agents on IoT hardware introduces attack surfaces absent from cloud-hosted orchestration. We present an empirical security...
Balazs Pejo
Federated learning offers a privacy-friendly collaborative learning framework, yet its success, like any joint venture, hinges on the contributions...
Qianlong Lan, Anuj Kaul, Shaun Jones +1 more
Agentic large language model systems increasingly automate tasks by retrieving URLs and calling external tools. We show that this workflow gives rise...
Vladimer Khasia
The pursuit of world model based artificial intelligence has predominantly relied on projecting high-dimensional observations into parameterized...
Idan Habler, Vineeth Sai Narajala, Stav Koren +2 more
Retrieval-Augmented Generation (RAG) systems are essential to contemporary AI applications, allowing large language models to obtain external...
Bruce W. Lee, Chen Yueh-Han, Tomek Korbak
Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior by...
Satyam Kumar Navneet, Joydeep Chandra, Yong Zhang
Large Language Models (LLMs) are increasingly used to ``professionalize'' workplace communication, often at the cost of linguistic identity. We...
Lan Zhang, Chengsi Liang, Zeming Zhuang +4 more
Semantic communication (SemCom) redefines wireless communication from reproducing symbols to transmitting task-relevant semantics. However, this...
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